{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recently proposed by [3].\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[3] Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_test:  (1000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch normalization: Forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:  [ 27.18502186  34.21455511  37.68611762]\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  mean:  [  5.99520433e-17   6.93889390e-17   8.32667268e-19]\n",
      "  std:  [ 0.99999999  1.          1.        ]\n",
      "After batch normalization (nontrivial gamma, beta)\n",
      "  means:  [ 11.  12.  13.]\n",
      "  stds:  [ 0.99999999  1.99999999  2.99999999]\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization\n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print('  means: ', a.mean(axis=0))\n",
    "print('  stds: ', a.std(axis=0))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, np.ones(D3), np.zeros(D3), {'mode': 'train'})\n",
    "print('  mean: ', a_norm.mean(axis=0))\n",
    "print('  std: ', a_norm.std(axis=0))\n",
    "\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print('After batch normalization (nontrivial gamma, beta)')\n",
    "print('  means: ', a_norm.mean(axis=0))\n",
    "print('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:  [ 1.01531428  1.01238373  0.97819988]\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=0))\n",
    "print('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch Normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.66746048753e-09\n",
      "dgamma error:  7.41722504069e-13\n",
      "dbeta error:  2.37944694996e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch Normalization: alternative backward (OPTIONAL, +3 points extra credit)\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For the sigmoid function, it turns out that you can derive a very simple formula for the backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can also derive a simple expression for the batch normalization backward pass if you work out derivatives on paper and simplify. After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster.\n",
    "\n",
    "NOTE: This part of the assignment is entirely optional, but we will reward 3 points of extra credit if you can complete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs2312n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the flag `use_batchnorm` is `True` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.26119551013\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 3.11e-06\n",
      "W3 relative error: 4.05e-10\n",
      "b1 relative error: 3.11e-07\n",
      "b2 relative error: 2.72e-07\n",
      "b3 relative error: 1.01e-10\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.89e-09\n",
      "gamma1 relative error: 6.96e-09\n",
      "gamma2 relative error: 2.41e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.99653322011\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.29e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 1.85e-08\n",
      "b2 relative error: 7.99e-07\n",
      "b3 relative error: 2.10e-10\n",
      "beta1 relative error: 6.65e-09\n",
      "beta2 relative error: 3.39e-09\n",
      "gamma1 relative error: 6.27e-09\n",
      "gamma2 relative error: 5.28e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            use_batchnorm=True)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 200) loss: 2.340974\n",
      "(Epoch 0 / 10) train acc: 0.137000; val_acc: 0.133000\n",
      "(Epoch 1 / 10) train acc: 0.319000; val_acc: 0.274000\n",
      "(Epoch 2 / 10) train acc: 0.407000; val_acc: 0.308000\n",
      "(Epoch 3 / 10) train acc: 0.464000; val_acc: 0.318000\n",
      "(Epoch 4 / 10) train acc: 0.512000; val_acc: 0.301000\n",
      "(Epoch 5 / 10) train acc: 0.555000; val_acc: 0.302000\n",
      "(Epoch 6 / 10) train acc: 0.618000; val_acc: 0.339000\n",
      "(Epoch 7 / 10) train acc: 0.685000; val_acc: 0.328000\n",
      "(Epoch 8 / 10) train acc: 0.735000; val_acc: 0.342000\n",
      "(Epoch 9 / 10) train acc: 0.755000; val_acc: 0.327000\n",
      "(Epoch 10 / 10) train acc: 0.819000; val_acc: 0.328000\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.129000; val_acc: 0.131000\n",
      "(Epoch 1 / 10) train acc: 0.245000; val_acc: 0.212000\n",
      "(Epoch 2 / 10) train acc: 0.316000; val_acc: 0.270000\n",
      "(Epoch 3 / 10) train acc: 0.340000; val_acc: 0.260000\n",
      "(Epoch 4 / 10) train acc: 0.382000; val_acc: 0.291000\n",
      "(Epoch 5 / 10) train acc: 0.405000; val_acc: 0.289000\n",
      "(Epoch 6 / 10) train acc: 0.461000; val_acc: 0.315000\n",
      "(Epoch 7 / 10) train acc: 0.481000; val_acc: 0.307000\n",
      "(Epoch 8 / 10) train acc: 0.551000; val_acc: 0.321000\n",
      "(Epoch 9 / 10) train acc: 0.594000; val_acc: 0.317000\n",
      "(Epoch 10 / 10) train acc: 0.611000; val_acc: 0.306000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "bn_solver.train()\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G2u/09tW2wUo+W0r278dspnqoKVCKMftKRERUWURkg6q2eG3HDB3Fzm2tVSmb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\n/X/+O14MbDGD0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d94acfe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o', label='baseline')\n",
    "plt.plot(bn_solver.loss_history, 'o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.train_acc_history, '-o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(solver.val_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.val_acc_history, '-o', label='batchnorm')\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers = {}\n",
    "solvers = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "  print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "  bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "  model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "  bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  bn_solver.train()\n",
    "  bn_solvers[weight_scale] = bn_solver\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  solver.train()\n",
    "  solvers[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Qu3+3Y6O1PhZm3lU/q/ESU+Cil6HvlXZA3UmjoeNZduaAyqpGC/ceTNBWz4KS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KtB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\n1kRERETi2P8HsAYLCLXMDncAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d910fec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(10, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Question:\n",
    "Describe the results of this experiment, and try to give a reason why the experiment gave the results that it did."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Answer:\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
